We also consider an experimental data estimation for PIV images, which is described in our previous work [1]}, to demonstrate the visualization techniques inside machine learned models.
As illustrated in figure REF \((b)\) , an autoencoder (AE)-type CNN is trained to estimate a velocity field from the corresponding particle image.
Note in passing that we adopt the AE-like structure, which is robust for noise and spatial sensitivity, to meet the requirement in handling experimental images properly [1]}.
